1. Field of the Invention
This invention relates generally to signal processing systems and, more particularly, to apparatus and methods for receiving and processing signals that share a common receiver frequency band at the same time, referred to as cochannel signals. Even two signals transmitted on slightly separated frequency bands may be xe2x80x9ccochannelxe2x80x9d signals as seen by a receiver operating to receive signals on a bandwidth that overlaps both of the signals. In a variety of signal processing applications, there is a need to recover information contained in such multiple, simultaneously received signals. In the context of this invention, the word xe2x80x9crecoverxe2x80x9d or xe2x80x9crecoveryxe2x80x9d encompasses separation of the received signals, xe2x80x9ccopyingxe2x80x9d the signals (i.e., retrieving any information contained in them), and, in some applications, combining signals received over multiple paths from a single source. The xe2x80x9csignalsxe2x80x9d may be electromagnetic signals transmitted in the atmosphere or in space, acoustic signals transmitted through liquids or solids, or other types of signals characterized by a time-varying parameter, such as the amplitude of a wave. In accordance with another aspect of the invention, signal processing includes transmission of cochannel signals.
In the environment of the present invention, signals are received by xe2x80x9csensors.xe2x80x9d A sensor is an appropriately selected transducer for converting energy contained in the signal to a more easily manipulated form, such as electrical energy. In a radio communications application, electromagnetic signals are received by antennas and converted to electrical signals for further processing. After separation of the signals, they may be forwarded separately to transducers of a different type, such as loudspeakers, for converting the separated electrical signals into audio signals. In some applications, the signal content may be of less importance than the directions from which the signals were received, and in other applications the received signals may not be amenable to conversion to audible form. Instead, each recovered signal may contain information in digital form, or may contain information that is best understood by displaying it on a chart or electronic display device. Regardless of the environment in which the present invention is employed, it is characterized by multiple signals received by sensors simultaneously at the same or overlapping frequencies, the need to separate, recover, identify or combine the signals and, optionally, some type of output transducer to put the recovered information in a more easily discernible form.
2. Description of Related Art
Separation and recovery of signals of different frequencies is a routine matter and is handled by appropriate filtering of the received signals. It is common knowledge that television and radio signals are transmitted on different frequency bands and that one may select a desired signal by tuning a receiver to a specific channel. Separation and recovery of multiple signals transmitted at different frequencies and received simultaneously may be effected by similar means, using multiple tuned receivers in parallel. A more difficult problem, and the one with which the present invention is concerned, is how to separate and copy signals from multiple sources when the transmitted signals are at the same or overlapping frequencies. A single sensor, such as an antenna, is unable to distinguish between two or more received signals at the same frequency. However, antenna array technology provides for the separation of signals received from different directions. Basically, and as is well understood by antenna designers, an antenna array can be electronically xe2x80x9csteeredxe2x80x9d to transmit or receive signals to or from a desired direction. Moreover, the characteristics of the antenna array can be selectively modified to present xe2x80x9cnullsxe2x80x9d in the directions of signals other than that of the signal of interest. A further development in the processing of array signals was the addition of a control system to steer the array toward a signal of interest. This feature is called adaptive array processing and has been known for at least two to three decades. See, for example, a paper by B. Widrow, P. E. Mantey, L. J. Griffiths and B. B. Goode, xe2x80x9cAdaptive Antenna Systems,xe2x80x9d Proceedings of the IEEE, vol. 55, no. 12, pp. 2143-2159, December 1967. The steering characteristics of the antenna can be rapidly switched to receive signals from multiple directions in a xe2x80x9ctime-slicedxe2x80x9d manner. At one instant the antenna array is receiving a signal from one source and at the next instant, from a different source in a different direction, but information from the multiple sources is sampled rapidly enough to provide a complete record of all the received signals. It will be understood that, although steered antenna array technology was developed principally in the communications and radar fields, it is also applicable to the separation of acoustic and other types of signals.
In the communications field, signals take a variety of forms. Stated most generally, a communication signal typically includes a carrier signal at a selected frequency, on which is impressed or modulated an information signal. There are a large number of different modulation schemes, including amplitude modulation, in which the amplitude of the signal is varied in accordance with the value of an information signal, while the frequency stays constant, and frequency or phase modulation, in which the amplitude of the signal stays constant while its frequency or phase is varied to encode the information signal onto the carrier. Various forms of frequency and phase modulation are often referred to as constant modulus modulation methods, because the amplitude or modulus of the signal remains constant, at least in theory. In practice, the modulus is subject to distortion during transmission, and various devices, such as adaptive equalizers, are used to restore the constant-modulus characteristic of the signal at a receiver. The constant modulus algorithm was developed for this purpose and later applied to antenna arrays in a process called adaptive beam forming The following references are provided by way for further background on the constant modulus algorithm:
B. Agee, xe2x80x9cThe least-squares CMA: a new technique for rapid correction of constant modulus signals,xe2x80x9d Proc. ICASSP-86, pp. 953-956, Tokyo, Japan, April 1986.
R. Gooch, and J. Lundell, xe2x80x9cThe CM array, an adaptive beamformer for constant modulus signals,xe2x80x9d Proc. ICASSP-86, pp. 2523-2526, Tokyo, Japan, April 1986.
J. Lundell, and B. Widrow, xe2x80x9cApplications of the constant modulus adaptive algorithm to constant and non-constant modulus signals,xe2x80x9d Proc. Twenty-Second Asilomar Conference on Signals, Systems, and Computers, pp. 432-436, Pacific Grove, Calif., November 1988.
B. G. Agee, xe2x80x9cBlind separation and capture of communication signals using a multi-target constant modulus beamformer,xe2x80x9d Proc. 1989 IEEE Military Communications Conference, pp. 340-346, Boston, Mass. , October 1989.
R. D. Hughes, E. H. Lawrence, and L. P. Withers, Jr., xe2x80x9cA robust adaptive array for multiple narrowband sources,xe2x80x9d Proc. Twenty-Sixth Asilomar Conference on Signals, Systems, and Computers, pp. 35-39, Pacific Grove, Calif., November 1992.
J. J. Shynk and R. P. Gooch, xe2x80x9cConvergence properties of the multistage CMA adaptive beamformer,xe2x80x9d Proc. Twenty-Seventh Asilomar Conference on Signals, Systems, and Computers, pp. 622-626, Pacific Grove, Calif., November 1993.
The constant modulus algorithm works satisfactorily only for constant modulus signals, such as frequency-modulated (FM) signals or various forms of phase-shift keying (PSK) in which the phase is discretely or continuously varied to represent an information signal, but not for amplitude-modulated (AM) signals or modulation schemes that employ a combination of amplitude and phase modulation. There is a significant class of modulation schemes used known as M-ary quadrature amplitude modulation (QAM), used for transmitting digital data, whereby the instantaneous phase and amplitude of the carrier signal represents a selected data state. For example, 16-ary QAM has sixteen distinct phase-amplitude combinations. The xe2x80x9csignal constellationxe2x80x9d diagram for such a scheme has sixteen points arranged in a square matrix and lying on three separate constant-modulus circles. A signal constellation diagram is a convenient way of depicting all the possible signal states of a digitally modulated signal. In such a diagram, phase is represented by angular position and modulus is represented by distance from an origin.
The constant modulus algorithm has been applied with limited success to a 16-ary QAM scheme, because it can be represented as three separate constant-modulus signal constellations. However, for higher orders of QAM the constant modulus algorithm provides rapidly decreasing accuracy. For suppressed-carrier AM, the constant modulus approach fails completely in trying to recover cochannel AM signals. If there are multiple signals, the constant modulus algorithm yields signals with xe2x80x9ccross-talk,xe2x80x9d i.e. with information in the two signals being confused. For a single AM signal in the presence of noise, the constant-modulus algorithm yields a relatively noisy signal.
Because antenna arrays can be steered electronically to determine the directions of signal sources, it was perhaps not surprising that one well known form signal separator available prior to the present invention used direction finding as its basis. The approach is referred to as DF-aided copy, where DF means direction finding. This is an open-loop technique in which steering vectors that correspond to estimated signal source bearings are first determined; then used to extract waveforms of received signals. However, the direction finding phase of this approach requires a knowledge of the geometry and performance characteristics of the antenna array. Then steering vectors are fed forward to a beamformer, which nulls out the unwanted signals and steers one or more antenna beam(s) toward each selected source.
Prior to the present invention, some systems for cochannel signal separation used direction-finding (DF)-beamforming. Such systems separate cochannel signals by means of a multi-source (or cochannel) super-resolution direction finding algorithm that determines steering vectors and directions of arrival (DOAs) of multiple simultaneously detected cochannel signal sources. An algorithm determines beam-forming weight vectors from the set of steering vectors of the detected signals. The beamforming weight vectors are then used to recover the signals. Any of several well-known multi-source super-resolution DF algorithms can be used in such a system. Some of the better known ones are usually referred to by the acronyms MUSIC (MUltiple SIgnal Classification), ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques), Weighted Subspace Fitting (WSF), and Method of Direction Estimation (MODE).
MUSIC was developed in 1979 simultaneously by Ralph Schmidt in the United States and by Georges Bienvenu and Lawrence Kopp in France. The Schmidt work is described in R. O. Schmidt, xe2x80x9cMultiple emitter location and signal parameter estimation,xe2x80x9d Proc. RADC Spectrum Estimation Workshop, pp. 243-258, Rome Air Development Center, Griffiss Air Force Base, NY, Oct. 3-5, 1979. The Bienvenu work is described in G. Bienvenu and L. Kopp, xe2x80x9cPrincipe de la goniometrie passive adaptative,xe2x80x9d Proc. Colloque GRETSI, pp. 106/1-106/10, Nice, France, May 1979. MUSIC has been extensively studied and is the standard against which other super-resolution DF algorithms are compared.
ESPRIT is described in many publications in the engineering signal processing literature and is the subject of U.S. Pat. No. 4,750,147 entitled xe2x80x9cMethod for estimating signal source locations and signal parameters using an array of sensor pairs,xe2x80x9d issued to R. H. Roy III et al. ESPRIT was developed by Richard Roy, III, Arogyaswami Paulraj, and Prof. Thomas Kailath at Stanford University. It was presented as a super-resolution algorithm for direction finding in the following series of publications starting in 1986:
A. Paulraj, R. Roy, and T. Kailath, xe2x80x9cA subspace rotation approach to signal parameter estimation,xe2x80x9d Proc. IEEE, vol. 74, no. 4, pp. 1044-1045, July 1986.
R. Roy, A. Paulraj, and T. Kailath, xe2x80x9cESPRITxe2x80x94A subspace rotation approach to estimation of parameters of cisoids in noise,xe2x80x9d IEEE Trans. Acoust., Speech, and Signal Processing, vol. ASSP-34, no. 5, pp. 1340-1342, October 1986.
R. H. Roy, ESPRITxe2x80x94Estimation of Signal Parameters via Rotational Invariance Techniques, doctoral dissertation, Stanford University, Stanford, Calif., 1987.
R. Roy and T. Kailath, xe2x80x9cESPRITxe2x80x94Estimation of signal parameters via rotational invariance techniques,xe2x80x9d IEEE Trans. Acoust., Speech, and Signal Processing, vol. ASSP-37, no. 7, pp. 984-995, July 1989.
B. Ottersten, R. Roy, and T. Kailath, xe2x80x9cSignal waveform estimation in sensor array processing,xe2x80x9d Proc. Twenty-Third Asilomar Conference on Signals, Systems, and Computers, pp. 787-791, Pacific Grove, Calif., November 1989.
R. Roy and T. Kailath, xe2x80x9cESPRITxe2x80x94Estimation of signal parameters via rotational invariance techniques,xe2x80x9d Optical Engineering, vol. 29, no. 4, pp. 296-313, April 1990.
MUSIC and ESPRIT both require the same xe2x80x9cnarrowband array assumption,xe2x80x9d which is further discussed below in the detailed description of the invention, and both are modulation independent, a feature shared by all cochannel signal separation and recovery techniques that are based on the DF-beamforming method.
ESPRIT calculates two N-by-N covariance matrices, where N is the number of antenna elements, and solves a generalized eigenvalue problem numerically (instead of using a calibration table search, as MUSIC does). It does this for every block of input samples. MUSIC calculates a single N-by-N covariance matrix, performs an eigendecomposition, and searches a calibration table on every block of input array samples (snapshots).
MUSIC and ESPRIT have a number of shortcomings, some of which are discussed in the following paragraphs.
ESPRIT was successfully marketed based on a single, key advantage over MUSIC. Unlike MUSIC, ESPRIT did not require array calibration. In ESPRIT, the array calibration requirement was eliminated, and a different requirement on the antenna array was substituted. The new requirement was that the array must have a certain geometrical property. Specifically the array must consist of two identical sub-arrays, one of which is offset from the other by a known displacement vector. In addition, ESPRIT makes the assumption that the phases of received signals at one sub-array are related to the phases at the other sub-array in an ideal theoretical way.
Another significant disadvantage of ESPRIT is that, although it purports not to use array calibration, it has an array manifold assumption hidden in the theoretical phase relation between sub-arrays. xe2x80x9cArray manifoldxe2x80x9d is a term used in antenna design to refer to a multiplicity of physical antenna parameters that, broadly speaking, define the performance characteristics of the array.
A well known difficulty with communication systems, especially in an urban environment, is that signals from a single source may be received over multiple paths that include reflections from buildings and other objects. The multiple paths may interpose different time delays, phase changes and amplitude changes on the transmitted signals, rendering reception more difficult, and transmission uncertain. This difficulty is referred to as the multipath problem. It is one that has not been adequately addressed by signal processing systems of the prior art.
Neither MUSIC nor ESPRIT can operate in a coherent multipath environment without major added complexity. A related problem is that, in a signal environment devoid of coherent multipath, no DF-beamforming method can separate signals from sources that are collinear with the receiving array, i.e. signal sources that are in line with the array and have zero angular separation. Even in a coherent multipath environment, DF-beamforming methods like MUSIC and ESPRIT cannot separate and recover cochannel signals from collinear sources.
Another difficulty with ESPRIT is that it requires two antenna sub-arrays and is highly sensitive to mechanical positioning of the two sub-arrays, and to the electromagnetic matching of each antenna in one sub-array with its counterpart in the other sub-array. Also ESPRIT requires a 2N-channel receiver, where N is the number of antenna elements, and is highly sensitive to channel matching.
Another significant drawback in both MUSIC and ESPRIT is that they fail abruptly when the number of signals detected exceeds the capacity, N, equal to the number of antennas in the case of MUSIC, or half the number of antennas in the case of ESPRIT.
A fundamental problem with both MUSIC and ESPRIT is that they use open-loop feed-forward computations, in which errors in the determined steering vectors are uncorrected, uncorrectable, and propagate into subsequent calculations. As a consequence of the resultant inaccurate steering vectors, MUSIC and ESPRIT have poorcross-talk rejection, as measured by signal-to-interference-plus-noise ratio (SINR) at the signal recovery output ports.
ESPRIT is best suited to ground based systems where its antenna requirements are best met and significant computational resources are available. MUSIC has simpler antenna array requirements and lends itself to a wider range of platforms, but also needs significant computational resources.
Another limitation of most signal recovery systems of the prior art is that they rely on first-order and second-order statistical moments of the received signal data. A moment is simply a statistical quantity derived from the original data by mathematical processing at some level. An average or mean value of the several signals received at a given time is an example of a first-order moment. The average of the squares of the signal values (proportional to signal powers) is an example of a second-order moment. Even if one considers just one signal and a noise component, computing the average of the sum of the squares produces a cross-term involving the product of signal and noise components. Typically, engineers have managed to find a way to ignore the cross-term by assuming that the signal and the noise components are statistically independent. At a third-order level of statistics, one has to assume that the signal and noise components have zero mean values in order to eliminate the cross-terms in the third-order moment. For the fourth-order and above, the computations become very complex and are not easily simplified by assumptions. In most prior art signal analysis systems, engineers have made the gross assumption that the nature of all signals is Gaussian and that there is no useful information in the higher-order moments. Higher-order statistics have been long recognized in other fields and there is recent literature suggesting their usefulness in signal recovery. Prior to this invention, cumulant-based solutions have been proposed to address the xe2x80x9cblindxe2x80x9d signal separation problem, i.e. the challenge to recover cochannel signals without knowledge of antenna array geometry or calibration data. See, for example, the following references:
J.-F. Cardoso, xe2x80x9cSource separation using higher order moments,xe2x80x9d Proc. ICASSP-89, pp. 2109-2112, Glasgow, Scotland, May 1989.
J.-F. Cardoso, xe2x80x9cEigen-structure of the fourth-order cumulant tensor with application to the blind source separation problem,xe2x80x9d Proc. ICASSP-90, pp. 2655-2658, Albuquerque, N. Mex., April 1990.
J.-F. Cardoso, xe2x80x9cSuper-symmetric decomposition of the fourth-order cumulant tensor: blind identification of more sources than sensors,xe2x80x9d Proc. ICASSP-91, pp. 3109-3112, Toronto, Canada, May 1991.
J.-F. Cardoso, xe2x80x9cHigher-order narrowband array processing,xe2x80x9d International Signal Processing Workshop on Higher Order Statistics, pp. 121-130, Chamrousse-France, Jul. 10-12, 1991.
J.-F. Cardoso, xe2x80x9cBlind beamforming for non-Gaussian sources,xe2x80x9d IEE Proceedings Part F, vol. 140, no. 6, pp. 362-370, December 1993.
P. Comon, xe2x80x9cSeparation of stochastic processes,xe2x80x9d Proc. Vail Workshop on Higher-Order Spectral Analysis, pp. 174-179, Vail, Colo., USA, June 1989.
P. Comon, xe2x80x9cIndependent component analysis,xe2x80x9d Proc. of Intl. Workshop on Higher-Order Statistics, pp. 111-120, Chamrousse, France, 1991.
P. Comon, C. Jutten, and J. Herault, xe2x80x9cBlind separation of sources, part II: problems statement,xe2x80x9d Signal Processing, vol. 24, no. 1, pp. 11-20, July 1991.
E. Chaumette, P. Comon, and D. Muller, xe2x80x9cICA-based technique for radiating sources estimation: application to airport surveillance,xe2x80x9d IEE Proceedings Part F, vol. 140, no. 6, pp. 395-401, December 1993.
Z. Ding, xe2x80x9cA new algorithm for automatic beamforming,xe2x80x9d Proc. Twenty-Fifth Asilomar Conference on Signals, Systems, and Computers, pp. 689-693, Pacific Grove, Calif., November 1991.
M. Gaeta and J.-L. Lacoume, xe2x80x9cSource separation without a-priori knowledge: the maximum likelihood solution,xe2x80x9d Proc. EUSIPCO, pp. 621-624, 1990.
E. Moreau, and O. Macchi, xe2x80x9cNew self-adaptive algorithms for source separation based on contrast functions,xe2x80x9d Proc. IEEE SP Workshop on Higher-Order Statistics, pp. 215-219, Lake Tahoe, USA, June 1993.
P. Ruiz, and J. L. Lacoume, xe2x80x9cExtraction of independent sources from correlated inputs: a solution based on cumulants,xe2x80x9d Proc. Vail Workshop on Higher-Order Spectral Analysis, pp. 146-151, Vail, Colo., USA, June 1989.
E. H. Satorius, J. J. Mulligan, Norman E. Lay, xe2x80x9cNew criteria for blind adaptive arrays,xe2x80x9d Proc. Twenty-Seventh Asilomar Conference on Signals, Systems, and Computers, pp. 633-637, Pacific Grove, Calif., November 1993.
L. Tong, R. Liu, V. Soon, and Y. Huang, xe2x80x9cIndeterminacy and identifiability of blind identification,xe2x80x9d IEEE Trans. Circuits and Systems, vol. 38, pp. 499-509, May 1991.
L. Tong, Y. Inouye and R. Liu, xe2x80x9cWaveform preserving blind estimation of multiple independent sources,xe2x80x9d IEEE Trans. Signal Processing, vol. 41, no. 7, pp. 2461-2470, July 1993.
However, all of these approaches to blind signal recovery address the static case in which a batch of data is given to a processor, which then determines the steering vectors and exact waveforms. These prior approaches do not have the ability to identify new sources that appear or existing sources that are turned off. In addition, previously proposed algorithms require multiple levels of eigendecomposition of array covariance and cumulant matrices. Their convergence to reliable solutions depends on the initialization and utilization of the cumulant matrices that can be derived from array measurements. Furthermore, previous cumulant-based algorithms have convergence problems in the case of identically modulated sources in general.
Ideally, a system for receiving and processing multiple cochannel signals should make use of statistics of the measurements, and should not need to rely on knowledge of the geometry or array manifold of the sensors, i.e., the array calibration data. Also, the system should be able to receive and process cochannel signals regardless of their modulation or signal type, e.g. it should not be limited to constant-modulus signals. More generally, the ideal cochannel signal processing system should not be limited to any modulation properties, such as baud rate or exact center frequency. Any system that is limited by these properties has only a limited range of source types that can be separated, and is more suitable for interference suppression in situations where the desired signal properties are well known. Another desirable property of the ideal cochannel signal receiving and processing system is that it should operate in a dynamic way, identifying new signal sources that appear and identifying sources that disappear. Another desirable characteristic is a very high speed of operation allowing received signals to be processed in real time. As will shortly become apparent, the present invention meets and exceeds these ideal characteristics for cochannel signal processing.
The present invention resides in a system or method for processing cochannel signals received at a sensor array and producing desired recovered signals or parameters as outputs. In the context of this specification, xe2x80x9ccochannelxe2x80x9d signals are that overlap in frequency, as viewed from a receiver of the signals. Even signals that are transmitted in separate, but closely spaced, frequency bands may be cochannel signals as viewed from a receiver operating in bandwidth wide enough to overlap both of the signals. A key aspect of the invention is that it is capable of separating and recovering multiple cochannel signals very rapidly using only sensor array signals, without knowledge of sensor array geometry and array manifold, (e.g. array calibration data), and without regard to the signal type or modulation. If array calibration data are available, the system also provides direction-of-arrival parameters for each signal source. The invention inherently combines coherent multipath components of a received signal and as a result achieves improved performance in the presence of multipath. One embodiment of the invention also includes a transmitter, which makes use of estimated generalized steering vectors generated while separating and recovering received signals, in order to generate appropriate steering vectors for transmitted signals, to ensure that transmitted signals intended for a particular signal source traverse generally the same path or paths that were followed by signals received from the same signal source.
Briefly, and in general terms, the system of the invention comprises a signal receiving system, including means for generating a set of conditioned receiver signals from received signals of any modulation or type; an estimated generalized steering vector (EGSV) generator, for computing an EGSV that results in optimization of a utility function that depends on fourth or higher even-order statistical cumulants derived from the received signals, the EGSV being indicative of a combination of signals received at the sensors from a signal source; and a supplemental computation module, for deriving at least one output quantity of interest from the conditioned receiver signals and the EGSV.
The basic invention as described in the preceding paragraph employs one of three basic methods for computing EGSVs: two iterative methods and one direct computation method. In the first iterative method, the system includes a linear combiner, for repeatedly computing a single channel combined signal from the conditioned receiver signals and an EGSV; means for supplying an initial EGSV to the linear combiner, to produce the initial output of a single channel combined signal; an EGSV computation module, for computing successive values of the EGSV from successive values of the single channel combined signal received from the linear combiner and the conditioned receiver signals; and means for feeding the successive values of the EGSV back to the linear combiner for successive iteration cycles. Also included is means for terminating iterative operation upon convergence of the EGSV to a sufficiently accurate value.
If the second iterative method is used, the system includes a cross-cumulant matrix computation module, for generating a matrix of cross-cumulants of all combinations of the conditioned receiver signals; a structured quadratic form computation module, for computing successive cumulant strength functions derived from successive EGSVs and the cross-cumulant matrix; means for supplying an initial EGSV to the structured quadratic form computation module, to produce the initial output of a cumulant strength function; an ESGV computation module, for generating successive EGSVs from successive cumulant strength functions received from the structured quadratic form computation module; means for feeding the successive values of the EGSV back to the structured quadratic form computation module for successive iteration cycles; and means for terminating iterative operation upon convergence of the EGSV to a sufficiently accurate value.
Finally, if the direct computation method is used, the system includes a cross-cumulant matrix computation module, for generating a matrix of cross-cumulants of all combinations of the conditioned receiver signals; and an EGSV computation module for computing the EGSV directly from the cross-cumulant matrix by solving a fourth degree polynomial equation.
Regardless which of the foregoing variants is employed, signal processing may employ one of several different cumulant recovery (CURE) techniques. In a first of these techniques, the means for generating the set of conditioned signals includes a covariance matrix computation module, an eigendecomposition module for generating the eigenstructure of the covariance matrix and an estimate of the number of signal sources, and a transformation matrix for conditioning the receiver signals. An EGSV generator then employs signals output by the eigendecomposition module to compute EGSVs. This technique is referred to in this specification as the eigenCURE or eCURE system.
An alternate processing technique uses covariance inversion of the received signals and is referred to as the CiCURE system. In this approach, the means for generating the set of conditioned signals includes a covariance matrix computation module and a matrix decomposition module, for generating the inverse covariance matrix and a transformation matrix for conditioning the receiver signals. An EGSV generator then employs signals output by the eigendecomposition module to compute EGSVs. The system further includes a beamformer, for generating a recovered signal from the receiver signals by using the EGSV(s) and the matrix obtained from the matrix decomposition module.
Yet another processing technique is referred to as pipelined cumulant recovery, or pipeCURE. The means for generating the set of conditioned signals includes a covariance matrix computation module, an eigendecomposition module for generating an estimate of the number of signal sources, a transformation matrix for conditioning the receiver signals, and an eigenstructure derived from the receiver signals. Again, the EGSV generator employs signals output by the eigendecomposition module to compute EGSVs. Processing is on a block-by-block basis, and the system further comprises a multiple port signal recovery unit, including means for matching current EGSVs with EGSVs from a prior data block to impose waveform continuity from block to block.
Another variant that can be used in any of these processing techniques involves the manner in which initial EGSVs are computed at the start of processing a new block of data. In accordance with this aspect of the invention, the initial values of EGSVs for each new processing block are computed by combining a prior block EGSV and a cumulant vector derived from the utility function used in the EGSV generator. More specifically, the means for combining takes the sum of the prior block EGSV multiplied by a first factor, and the cumulant vector multiplied by a second factor. The first and second factors may be selected to provide an initial EGSV that anticipates and compensates for movement of a signal source.
In a practical embodiment of the invention, the system functions to separate a plurality (P) of received cochannel signals. If the first iterative method is employed, there are multiple EGSV generators (P in number), including P EGSV computation modules and P linear combiners, for generating an equal plurality (P) of EGSVs associated with P signal sources. The supplemental computation module functions to recover P separate received signals from the P generalized steering vectors and the conditioned receiver signals. More specifically, the supplemental computation module includes a recovery beamformer weight vector computation module, for generating from all of the EGSVs a plurality (P) of receive weight vectors, and a plurality (P) of recovery beamformers, each coupled to receive one of the P receive weight vectors and the conditioned receiver signals, for generating a plurality (P) of recovered signals.
For recovery of multiple signals using the second iterative method, there is a plurality (P) of EGSV generators, including P EGSV computation modules and P structured quadratic form computation modules, for generating an equal plurality (P) of EGSVs associated with P signal sources. Again, the supplemental computation module includes a recovery beamformer weight vector computation module, for generating from all of the EGSVs a plurality (P) of receive weight vectors, and a plurality (P) of recovery beamformers, each coupled to receive one of the P receive weight vectors and the conditioned receiver signals, for generating a plurality (P) of recovered signals.
If the direct processing method is used to separate two signals, the ESGV computation module generates two EGSVs from the cross-cumulant matrix data; and the supplemental computation module functions to recover two separate received signals from the two generalized steering vectors and the conditioned receiver signals. The supplemental computation module includes a recovery beamformer weight vector computation module, for generating from both of the EGSVs two receive weight vectors, and two recovery beamformers, each coupled to receive one of the receive weight vectors and the conditioned receiver signals, for generating two recovered signals.
Although the system of the invention operates in a xe2x80x9cblindxe2x80x9d sense, without knowledge of the geometry or calibration data of the sensor array, it will also function as a direction finder if array calibration data are available. Hence, in one embodiment of the invention, the system functions to derive the direction of arrival (DOA) of a received signal; and the supplemental computation module includes a memory for storing sensor array calibration data, and means for deriving the DOA of a received signal from its associated steering vector and the stored sensor array calibration data. More specifically, the sensor array calibration data includes a table associating multiple DOA values with corresponding steering vectors; and the means for deriving the DOA includes means for performing a reverse table lookup function to obtain an approximated DOA value from a steering vector supplied by the generalized steering vector generator. The means for deriving the DOA may also include means for interpolating between two DOA values to obtain a more accurate result.
In another important embodiment of the invention, the supplemental computation module of the signal processing system also includes a transmitter, for generating transmit signal beamformer weights from the received signal beamformer weights, and for transmitting signals containing information in a direction determined by the transmit signal beamformer weights.
Other aspects of the invention pertain to various application of the basic cumulant recovery (CURE) signal processing engine described above. Some of these applications are summarized in the following paragraphs.
An important application of the invention is in two-way radio communication. Because CURE processing generates an estimated generalized steering vector not necessarily for each received signal, but for each signal source, the invention provides an important benefit when used in multipath conditions. Signals reaching a receiving antenna array over multiple paths will be combined in the CURE system if the received components are coherent, and the resultant generalized steering vector represents the combined effect of all the coherent multipath signals received at the antenna. This feature has a number of advantages. First, a radio receiving system using the CURE system is inherently immune to multipath problems encountered by conventional receivers. Second, by using generalized steering vectors, there can be an associated generalized null in the antenna directivity pattern, which can be used to null out an interfering signal having multipath structure in favor of a cochannel signal from another source. Third, the signal recovery method provides a diversity gain in the presence of multipath components, such that a stronger combined signal is received as compared with a system that discards all but one component. Fourth, the generalized steering vector concept allows multiple cochannel signals to be received and transmitted in the presence of multipath effects. Fifth, cochannel signal sources that are collinear with the receiver sensor array can be received and separated if there are multipath components.
In another aspect of the invention, the CURE signal separation system can be used to separate signals transmitted in different modes over a xe2x80x9cwaveguide,xe2x80x9d by which is meant any bounded propagation medium, such as a microwave waveguide, an optical waveguide, a coaxial cable, or even a twisted pair of conductors. Although the transmission modes may become mixed in the waveguide, the original signals are easily recovered in the CURE system.
In still another aspect of the invention, the CURE signal separation system can be used to separate signals recorded on closely space tracks on a recording medium. Crosstalk between the signals on adjacent tracks is eliminated by using the CURE system to effect recovery.
In yet another aspect of the invention, the CURE signal separation system can be used to extend the effective dynamic range of a receiver system.
In a further aspect of the invention, the CURE signal separation system can be used to perform a complex phase equalization function automatically, without knowledge of the amount of phase correction that is needed.
The CURE system may be modified to compensate for moving signal sources, and may also be modified to handle a wideband signal separation problem. The wideband signal separator includes multiple narrowband CURE systems, means for decomposing a wide band of signals into multiple narrowbands for processing, and means for combining the narrow bands again.